Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations10076
Missing cells396
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 MiB
Average record size in memory1.5 KiB

Variable types

Text10
Categorical2
Numeric1

Alerts

Estado is highly imbalanced (64.1%) Imbalance
CUIT has unique values Unique
NumeroEnte has 153 (1.5%) zeros Zeros

Reproduction

Analysis started2025-05-26 23:40:51.186503
Analysis finished2025-05-26 23:41:00.138975
Duration8.95 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct10076
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.9 KiB
2025-05-26T20:41:00.764763image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length11
Mean length10.968936
Min length3

Characters and Unicode

Total characters110523
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10076 ?
Unique (%)100.0%

Sample

1st row27236909900
2nd row30569211685
3rd row30711500363
4th row30708415487
5th row33712286089
ValueCountFrequency (%)
30708516852 1
 
< 0.1%
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
30569211685 1
 
< 0.1%
30711500363 1
 
< 0.1%
30708415487 1
 
< 0.1%
33712286089 1
 
< 0.1%
30583184305 1
 
< 0.1%
30708538317 1
 
< 0.1%
30521417311 1
 
< 0.1%
Other values (10066) 10066
99.9%
2025-05-26T20:41:02.129949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17633
16.0%
3 14944
13.5%
2 12785
11.6%
7 12346
11.2%
1 11377
10.3%
6 8863
8.0%
5 8370
7.6%
9 8322
7.5%
4 7960
7.2%
8 7692
7.0%
Other values (28) 231
 
0.2%

Nombre
Text

Distinct9040
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Memory size781.5 KiB
2025-05-26T20:41:02.857328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length135
Median length93
Mean length19.169909
Min length1

Characters and Unicode

Total characters193156
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9034 ?
Unique (%)89.7%

Sample

1st rowEMR VENTAS & SERVICIOS
2nd rowElectricidad Chiclana de R. Santoianni y O.S. Rodriguez
3rd rowLICICOM S.R.L.
4th rowYLUM S.A.
5th rowCOMPAÑÍA DE HIGIENE
ValueCountFrequency (%)
s.a 1772
 
5.9%
srl 1446
 
4.8%
sin 1034
 
3.5%
datos 1032
 
3.5%
de 899
 
3.0%
sa 842
 
2.8%
s.r.l 803
 
2.7%
y 478
 
1.6%
argentina 325
 
1.1%
servicios 265
 
0.9%
Other values (9967) 20940
70.2%
2025-05-26T20:41:03.807527image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19761
 
10.2%
A 14906
 
7.7%
S 11751
 
6.1%
R 9756
 
5.1%
E 9456
 
4.9%
I 9134
 
4.7%
O 7799
 
4.0%
L 7195
 
3.7%
. 6744
 
3.5%
a 6678
 
3.5%
Other values (97) 89976
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193156
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
19761
 
10.2%
A 14906
 
7.7%
S 11751
 
6.1%
R 9756
 
5.1%
E 9456
 
4.9%
I 9134
 
4.7%
O 7799
 
4.0%
L 7195
 
3.7%
. 6744
 
3.5%
a 6678
 
3.5%
Other values (97) 89976
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193156
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
19761
 
10.2%
A 14906
 
7.7%
S 11751
 
6.1%
R 9756
 
5.1%
E 9456
 
4.9%
I 9134
 
4.7%
O 7799
 
4.0%
L 7195
 
3.7%
. 6744
 
3.5%
a 6678
 
3.5%
Other values (97) 89976
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193156
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
19761
 
10.2%
A 14906
 
7.7%
S 11751
 
6.1%
R 9756
 
5.1%
E 9456
 
4.9%
I 9134
 
4.7%
O 7799
 
4.0%
L 7195
 
3.7%
. 6744
 
3.5%
a 6678
 
3.5%
Other values (97) 89976
46.6%
Distinct1804
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size659.3 KiB
2025-05-26T20:41:04.464441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9901747
Min length9

Characters and Unicode

Total characters100661
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique415 ?
Unique (%)4.1%

Sample

1st row04/10/2016
2nd row18/08/2016
3rd row15/09/2016
4th row24/08/2016
5th row26/10/2016
ValueCountFrequency (%)
sin 99
 
1.0%
datos 99
 
1.0%
03/11/2016 46
 
0.5%
14/11/2016 43
 
0.4%
17/11/2021 42
 
0.4%
17/11/2016 39
 
0.4%
01/11/2016 37
 
0.4%
23/11/2016 36
 
0.4%
10/01/2017 35
 
0.3%
07/11/2016 35
 
0.3%
Other values (1795) 9664
95.0%
2025-05-26T20:41:05.715367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22666
22.5%
/ 19954
19.8%
2 18140
18.0%
1 17919
17.8%
7 5493
 
5.5%
8 3480
 
3.5%
6 3441
 
3.4%
9 2749
 
2.7%
3 2352
 
2.3%
5 1800
 
1.8%
Other values (9) 2667
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22666
22.5%
/ 19954
19.8%
2 18140
18.0%
1 17919
17.8%
7 5493
 
5.5%
8 3480
 
3.5%
6 3441
 
3.4%
9 2749
 
2.7%
3 2352
 
2.3%
5 1800
 
1.8%
Other values (9) 2667
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22666
22.5%
/ 19954
19.8%
2 18140
18.0%
1 17919
17.8%
7 5493
 
5.5%
8 3480
 
3.5%
6 3441
 
3.4%
9 2749
 
2.7%
3 2352
 
2.3%
5 1800
 
1.8%
Other values (9) 2667
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22666
22.5%
/ 19954
19.8%
2 18140
18.0%
1 17919
17.8%
7 5493
 
5.5%
8 3480
 
3.5%
6 3441
 
3.4%
9 2749
 
2.7%
3 2352
 
2.3%
5 1800
 
1.8%
Other values (9) 2667
 
2.6%

Estado
Categorical

Imbalance 

Distinct10
Distinct (%)0.1%
Missing99
Missing (%)1.0%
Memory size686.0 KiB
Inscripto
7782 
Pre Inscripto
864 
Desactualizado Por Documentos Vencidos
833 
Desactualizado Por Mantencion Formulario
 
254
Desactualizado Por Clase
 
112
Other values (5)
 
132

Length

Max length40
Median length9
Mean length12.841034
Min length9

Characters and Unicode

Total characters128115
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowInscripto
2nd rowDesactualizado Por Documentos Vencidos
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 7782
77.2%
Pre Inscripto 864
 
8.6%
Desactualizado Por Documentos Vencidos 833
 
8.3%
Desactualizado Por Mantencion Formulario 254
 
2.5%
Desactualizado Por Clase 112
 
1.1%
Con Solicitud De Baja 81
 
0.8%
En Evaluacion 43
 
0.4%
Suspendido 6
 
0.1%
Inhabilitado 1
 
< 0.1%
Dar De Baja 1
 
< 0.1%
(Missing) 99
 
1.0%

Length

2025-05-26T20:41:06.302522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-26T20:41:06.835900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 8646
59.2%
desactualizado 1199
 
8.2%
por 1199
 
8.2%
pre 864
 
5.9%
documentos 833
 
5.7%
vencidos 833
 
5.7%
mantencion 254
 
1.7%
formulario 254
 
1.7%
clase 112
 
0.8%
de 82
 
0.6%
Other values (8) 338
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 14517
11.3%
c 11889
9.3%
s 11629
9.1%
i 11399
8.9%
n 11248
8.8%
r 11218
8.8%
t 11014
8.6%
p 8652
 
6.8%
I 8647
 
6.7%
4637
 
3.6%
Other values (20) 23265
18.2%
Distinct9655
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size668.3 KiB
2025-05-26T20:41:08.219933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length18
Mean length10.892517
Min length1

Characters and Unicode

Total characters109753
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9530 ?
Unique (%)94.6%

Sample

1st row2804357488
2nd row54114923-4922
3rd row45833433
4th row541149116641
5th row541147901907
ValueCountFrequency (%)
sin 213
 
2.0%
datos 213
 
2.0%
50719546 46
 
0.4%
11 27
 
0.3%
54 23
 
0.2%
9 13
 
0.1%
50719386 8
 
0.1%
1 7
 
0.1%
011 7
 
0.1%
42378281 6
 
0.1%
Other values (9718) 9885
94.6%
2025-05-26T20:41:10.544837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 16417
15.0%
1 15968
14.5%
5 13125
12.0%
0 11680
10.6%
2 10687
9.7%
3 10069
9.2%
6 7672
7.0%
9 6978
6.4%
7 6885
6.3%
8 6324
 
5.8%
Other values (19) 3948
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 16417
15.0%
1 15968
14.5%
5 13125
12.0%
0 11680
10.6%
2 10687
9.7%
3 10069
9.2%
6 7672
7.0%
9 6978
6.4%
7 6885
6.3%
8 6324
 
5.8%
Other values (19) 3948
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 16417
15.0%
1 15968
14.5%
5 13125
12.0%
0 11680
10.6%
2 10687
9.7%
3 10069
9.2%
6 7672
7.0%
9 6978
6.4%
7 6885
6.3%
8 6324
 
5.8%
Other values (19) 3948
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 16417
15.0%
1 15968
14.5%
5 13125
12.0%
0 11680
10.6%
2 10687
9.7%
3 10069
9.2%
6 7672
7.0%
9 6978
6.4%
7 6885
6.3%
8 6324
 
5.8%
Other values (19) 3948
 
3.6%
Distinct6215
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size908.5 KiB
2025-05-26T20:41:12.096197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length74
Median length72
Mean length28.697697
Min length9

Characters and Unicode

Total characters289158
Distinct characters92
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6133 ?
Unique (%)60.9%

Sample

1st rowsin datos
2nd rowEn C.A.B.A. con fecha 28/05/1980
3rd rowEn JUAN B JUSTO Nº 925, LANUS, PROV DE BS AS con fecha 22/06/2010
4th rowEn Rodriguez Peña 694 5º D, CABA con fecha 28/03/2003
5th rowEn BUENOS AIRES con fecha 08/07/2011
ValueCountFrequency (%)
en 6311
12.6%
fecha 6308
12.6%
con 6307
12.6%
sin 3770
 
7.5%
datos 3770
 
7.5%
buenos 2581
 
5.1%
aires 2576
 
5.1%
de 2136
 
4.3%
ciudad 1772
 
3.5%
autonoma 718
 
1.4%
Other values (5732) 13926
27.8%
2025-05-26T20:41:14.266443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40099
 
13.9%
n 20262
 
7.0%
a 15932
 
5.5%
o 14481
 
5.0%
0 14428
 
5.0%
c 13208
 
4.6%
e 12714
 
4.4%
/ 12619
 
4.4%
s 11814
 
4.1%
1 10529
 
3.6%
Other values (82) 123072
42.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 289158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
40099
 
13.9%
n 20262
 
7.0%
a 15932
 
5.5%
o 14481
 
5.0%
0 14428
 
5.0%
c 13208
 
4.6%
e 12714
 
4.4%
/ 12619
 
4.4%
s 11814
 
4.1%
1 10529
 
3.6%
Other values (82) 123072
42.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 289158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
40099
 
13.9%
n 20262
 
7.0%
a 15932
 
5.5%
o 14481
 
5.0%
0 14428
 
5.0%
c 13208
 
4.6%
e 12714
 
4.4%
/ 12619
 
4.4%
s 11814
 
4.1%
1 10529
 
3.6%
Other values (82) 123072
42.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 289158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
40099
 
13.9%
n 20262
 
7.0%
a 15932
 
5.5%
o 14481
 
5.0%
0 14428
 
5.0%
c 13208
 
4.6%
e 12714
 
4.4%
/ 12619
 
4.4%
s 11814
 
4.1%
1 10529
 
3.6%
Other values (82) 123072
42.6%

TipoSocietario
Categorical

Distinct11
Distinct (%)0.1%
Missing99
Missing (%)1.0%
Memory size1.0 MiB
Persona Física
3647 
Sociedad Anónima
2845 
Sociedad Responsabilidad Limitada
2342 
Otras Formas Societarias
 
344
Persona Jurídica Extranjero Sin Sucursal
 
256
Other values (6)
543 

Length

Max length40
Median length38
Mean length20.20427
Min length12

Characters and Unicode

Total characters201578
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPersona Física
2nd rowSociedades De Hecho
3rd rowSociedad Responsabilidad Limitada
4th rowSociedad Anónima
5th rowSociedad Responsabilidad Limitada

Common Values

ValueCountFrequency (%)
Persona Física 3647
36.2%
Sociedad Anónima 2845
28.2%
Sociedad Responsabilidad Limitada 2342
23.2%
Otras Formas Societarias 344
 
3.4%
Persona Jurídica Extranjero Sin Sucursal 256
 
2.5%
Organismo Publico 200
 
2.0%
Cooperativas 187
 
1.9%
Sociedades De Hecho 117
 
1.2%
Persona Física Extranjero No Residente 24
 
0.2%
Unión Transitoria de Empresas 14
 
0.1%
(Missing) 99
 
1.0%

Length

2025-05-26T20:41:14.841175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sociedad 5187
22.1%
persona 3927
16.8%
física 3671
15.7%
anónima 2845
12.1%
responsabilidad 2342
10.0%
limitada 2342
10.0%
otras 344
 
1.5%
formas 344
 
1.5%
societarias 344
 
1.5%
extranjero 280
 
1.2%
Other values (17) 1814
 
7.7%

Most occurring characters

ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 201578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27900
13.8%
i 23043
11.4%
d 17931
 
8.9%
s 14142
 
7.0%
o 13473
 
6.7%
13463
 
6.7%
e 12839
 
6.4%
n 12762
 
6.3%
c 10150
 
5.0%
r 6463
 
3.2%
Other values (28) 49412
24.5%

NumeroEnte
Real number (ℝ)

Zeros 

Distinct9923
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460136.61
Minimum0
Maximum1416772
Zeros153
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size78.8 KiB
2025-05-26T20:41:15.773054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5005.75
Q1195257.5
median472098
Q3646229.25
95-th percentile1109305.8
Maximum1416772
Range1416772
Interquartile range (IQR)450971.75

Descriptive statistics

Standard deviation318335.56
Coefficient of variation (CV)0.69182838
Kurtosis0.035584732
Mean460136.61
Median Absolute Deviation (MAD)201826.5
Skewness0.54686109
Sum4.6363364 × 109
Variance1.0133753 × 1011
MonotonicityNot monotonic
2025-05-26T20:41:16.439396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 153
 
1.5%
1241 2
 
< 0.1%
3617 1
 
< 0.1%
389378 1
 
< 0.1%
220801 1
 
< 0.1%
8055 1
 
< 0.1%
370562 1
 
< 0.1%
254245 1
 
< 0.1%
237357 1
 
< 0.1%
1330129 1
 
< 0.1%
Other values (9913) 9913
98.4%
ValueCountFrequency (%)
0 153
1.5%
103 1
 
< 0.1%
315 1
 
< 0.1%
320 1
 
< 0.1%
321 1
 
< 0.1%
326 1
 
< 0.1%
334 1
 
< 0.1%
372 1
 
< 0.1%
375 1
 
< 0.1%
379 1
 
< 0.1%
ValueCountFrequency (%)
1416772 1
< 0.1%
1409657 1
< 0.1%
1409422 1
< 0.1%
1408203 1
< 0.1%
1405282 1
< 0.1%
1393718 1
< 0.1%
1392476 1
< 0.1%
1391241 1
< 0.1%
1391213 1
< 0.1%
1390752 1
< 0.1%
Distinct9885
Distinct (%)99.1%
Missing99
Missing (%)1.0%
Memory size3.6 MiB
2025-05-26T20:41:18.154441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length233
Median length196
Mean length145.5222
Min length8

Characters and Unicode

Total characters1451875
Distinct characters104
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9809 ?
Unique (%)98.3%

Sample

1st rowCACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100
2nd rowAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAW
3rd rowAV CORRIENTES 4709, piso N° 3, depto N° 36, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414
4th rowRodriguez Peña 694, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1020ADN
5th rowO'Higgins 3550, localidad OLIVOS, departamento VICENTE LOPEZ, provincia Buenos Aires, Argentina, código postal 1636
ValueCountFrequency (%)
buenos 13379
 
6.5%
aires 13379
 
6.5%
de 12542
 
6.1%
ciudad 10978
 
5.4%
autónoma 10918
 
5.3%
localidad 9774
 
4.8%
provincia 9772
 
4.8%
departamento 9771
 
4.8%
código 9770
 
4.8%
postal 9770
 
4.8%
Other values (9094) 94243
46.1%
2025-05-26T20:41:20.397918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451875
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
194320
 
13.4%
a 102930
 
7.1%
o 87758
 
6.0%
i 82519
 
5.7%
d 78591
 
5.4%
e 78354
 
5.4%
n 69197
 
4.8%
A 63920
 
4.4%
t 56952
 
3.9%
, 55769
 
3.8%
Other values (94) 581565
40.1%
Distinct9851
Distinct (%)98.7%
Missing99
Missing (%)1.0%
Memory size3.5 MiB
2025-05-26T20:41:21.936452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length233
Median length198
Mean length144.03648
Min length6

Characters and Unicode

Total characters1437052
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9768 ?
Unique (%)97.9%

Sample

1st rowCACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100
2nd rowAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAW
3rd rowSAN BLAS 2257, piso N° PB, depto N° PB, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1416
4th rowFamatina 3933, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1437IOU
5th rowObispo San Alberto 2975, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419
ValueCountFrequency (%)
aires 13099
 
6.5%
buenos 13098
 
6.5%
de 11968
 
6.0%
ciudad 10371
 
5.2%
autónoma 10307
 
5.1%
localidad 9802
 
4.9%
provincia 9801
 
4.9%
departamento 9800
 
4.9%
código 9760
 
4.9%
postal 9760
 
4.9%
Other values (8989) 93154
46.4%
2025-05-26T20:41:24.158480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1437052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
190943
 
13.3%
a 101638
 
7.1%
o 86191
 
6.0%
i 81279
 
5.7%
e 76809
 
5.3%
d 76450
 
5.3%
n 68395
 
4.8%
A 63807
 
4.4%
t 56056
 
3.9%
, 55253
 
3.8%
Other values (93) 580231
40.4%
Distinct5999
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Memory size743.9 KiB
2025-05-26T20:41:25.090542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length44
Mean length18.588428
Min length9

Characters and Unicode

Total characters187297
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5929 ?
Unique (%)58.8%

Sample

1st rowsin datos
2nd rowelectricidadchiclana@e-chiclana.com.ar
3rd rowLICICOM.SRL@GMAIL.COM
4th rowylumsa@ylumsa.com.ar
5th rowinfo@companiadehigiene.com.ar
ValueCountFrequency (%)
sin 3994
28.4%
datos 3994
28.4%
fecootraunfv@hotmail.com 6
 
< 0.1%
ldurandeu@hotmail.com 5
 
< 0.1%
federacion1demayo@gmail.com 4
 
< 0.1%
ds@fasempresas.com.ar 4
 
< 0.1%
info@wasserberg.com 3
 
< 0.1%
clientesguadalquivir@fibertel.com.ar 3
 
< 0.1%
jlancioni@expresolancioni.com 3
 
< 0.1%
pbollag@wasserberg.com 3
 
< 0.1%
Other values (5988) 6051
43.0%
2025-05-26T20:41:26.740860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20133
 
10.7%
o 18683
 
10.0%
i 14863
 
7.9%
s 14518
 
7.8%
n 11653
 
6.2%
c 10954
 
5.8%
r 10597
 
5.7%
. 10154
 
5.4%
m 9912
 
5.3%
t 9623
 
5.1%
Other values (57) 56207
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 187297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 20133
 
10.7%
o 18683
 
10.0%
i 14863
 
7.9%
s 14518
 
7.8%
n 11653
 
6.2%
c 10954
 
5.8%
r 10597
 
5.7%
. 10154
 
5.4%
m 9912
 
5.3%
t 9623
 
5.1%
Other values (57) 56207
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 187297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 20133
 
10.7%
o 18683
 
10.0%
i 14863
 
7.9%
s 14518
 
7.8%
n 11653
 
6.2%
c 10954
 
5.8%
r 10597
 
5.7%
. 10154
 
5.4%
m 9912
 
5.3%
t 9623
 
5.1%
Other values (57) 56207
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 187297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 20133
 
10.7%
o 18683
 
10.0%
i 14863
 
7.9%
s 14518
 
7.8%
n 11653
 
6.2%
c 10954
 
5.8%
r 10597
 
5.7%
. 10154
 
5.4%
m 9912
 
5.3%
t 9623
 
5.1%
Other values (57) 56207
30.0%
Distinct2842
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size672.1 KiB
2025-05-26T20:41:28.187312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length9
Mean length9.5732434
Min length1

Characters and Unicode

Total characters96460
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2758 ?
Unique (%)27.4%

Sample

1st rowsin datos
2nd rowsin datos
3rd rowsin datos
4th row3886
5th row8196
ValueCountFrequency (%)
datos 7071
34.3%
sin 7071
34.3%
libro 291
 
1.4%
tomo 253
 
1.2%
folio 221
 
1.1%
155
 
0.8%
105
 
0.5%
a 98
 
0.5%
de 93
 
0.5%
srl 49
 
0.2%
Other values (3546) 5206
25.3%
2025-05-26T20:41:30.400323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 14322
14.8%
10537
10.9%
o 8109
 
8.4%
i 7558
 
7.8%
a 7315
 
7.6%
t 7259
 
7.5%
d 7213
 
7.5%
n 7207
 
7.5%
1 3011
 
3.1%
2 2032
 
2.1%
Other values (70) 21897
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 14322
14.8%
10537
10.9%
o 8109
 
8.4%
i 7558
 
7.8%
a 7315
 
7.6%
t 7259
 
7.5%
d 7213
 
7.5%
n 7207
 
7.5%
1 3011
 
3.1%
2 2032
 
2.1%
Other values (70) 21897
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 14322
14.8%
10537
10.9%
o 8109
 
8.4%
i 7558
 
7.8%
a 7315
 
7.6%
t 7259
 
7.5%
d 7213
 
7.5%
n 7207
 
7.5%
1 3011
 
3.1%
2 2032
 
2.1%
Other values (70) 21897
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 14322
14.8%
10537
10.9%
o 8109
 
8.4%
i 7558
 
7.8%
a 7315
 
7.6%
t 7259
 
7.5%
d 7213
 
7.5%
n 7207
 
7.5%
1 3011
 
3.1%
2 2032
 
2.1%
Other values (70) 21897
22.7%
Distinct4093
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size659.3 KiB
2025-05-26T20:41:31.782708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length50
Median length9
Mean length9.104605
Min length1

Characters and Unicode

Total characters91738
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4038 ?
Unique (%)40.1%

Sample

1st rowsin datos
2nd rowsin datos
3rd row53538
4th row1718555
5th row1846001
ValueCountFrequency (%)
sin 5849
31.2%
datos 5849
31.2%
libro 429
 
2.3%
tomo 245
 
1.3%
de 172
 
0.9%
a 128
 
0.7%
102
 
0.5%
srl 102
 
0.5%
del 54
 
0.3%
folio 52
 
0.3%
Other values (4518) 5769
30.8%
2025-05-26T20:41:34.356754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 11870
12.9%
8675
 
9.5%
o 6842
 
7.5%
i 6401
 
7.0%
d 6093
 
6.6%
a 6059
 
6.6%
t 6030
 
6.6%
n 5991
 
6.5%
1 4794
 
5.2%
0 2546
 
2.8%
Other values (75) 26437
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 11870
12.9%
8675
 
9.5%
o 6842
 
7.5%
i 6401
 
7.0%
d 6093
 
6.6%
a 6059
 
6.6%
t 6030
 
6.6%
n 5991
 
6.5%
1 4794
 
5.2%
0 2546
 
2.8%
Other values (75) 26437
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 11870
12.9%
8675
 
9.5%
o 6842
 
7.5%
i 6401
 
7.0%
d 6093
 
6.6%
a 6059
 
6.6%
t 6030
 
6.6%
n 5991
 
6.5%
1 4794
 
5.2%
0 2546
 
2.8%
Other values (75) 26437
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 11870
12.9%
8675
 
9.5%
o 6842
 
7.5%
i 6401
 
7.0%
d 6093
 
6.6%
a 6059
 
6.6%
t 6030
 
6.6%
n 5991
 
6.5%
1 4794
 
5.2%
0 2546
 
2.8%
Other values (75) 26437
28.8%

Interactions

2025-05-26T20:40:56.700150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-26T20:41:34.700040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
EstadoNumeroEnteTipoSocietario
Estado1.0000.0530.190
NumeroEnte0.0531.0000.123
TipoSocietario0.1900.1231.000

Missing values

2025-05-26T20:40:57.516230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-26T20:40:58.795692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-26T20:40:59.667882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTelefonicoContactoConstitucionTipoSocietarioNumeroEnteDomicilioLegalDomicilioEspecialCorreoInstitucionalRegistroPublicoComercioInspeccionGeneralJusticia
027236909900EMR VENTAS & SERVICIOS04/10/2016Inscripto2804357488sin datosPersona Física433508.0CACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100CACIQUE FRANCISCO 1398, localidad TRELEW, departamento RAWSON, provincia Chubut, Argentina, código postal 9100sin datossin datossin datos
130569211685Electricidad Chiclana de R. Santoianni y O.S. Rodriguez18/08/2016Desactualizado Por Documentos Vencidos54114923-4922En C.A.B.A. con fecha 28/05/1980Sociedades De Hecho3617.0Av. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAWAv. Boedo 1986, piso N° P.B., localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1239AAWelectricidadchiclana@e-chiclana.com.arsin datossin datos
230711500363LICICOM S.R.L.15/09/2016Inscripto45833433En JUAN B JUSTO Nº 925, LANUS, PROV DE BS AS con fecha 22/06/2010Sociedad Responsabilidad Limitada389378.0AV CORRIENTES 4709, piso N° 3, depto N° 36, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414SAN BLAS 2257, piso N° PB, depto N° PB, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1416LICICOM.SRL@GMAIL.COMsin datos53538
330708415487YLUM S.A.24/08/2016Inscripto541149116641En Rodriguez Peña 694 5º D, CABA con fecha 28/03/2003Sociedad Anónima214190.0Rodriguez Peña 694, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1020ADNFamatina 3933, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1437IOUylumsa@ylumsa.com.ar38861718555
433712286089COMPAÑÍA DE HIGIENE26/10/2016Inscripto541147901907En BUENOS AIRES con fecha 08/07/2011Sociedad Responsabilidad Limitada471565.0O'Higgins 3550, localidad OLIVOS, departamento VICENTE LOPEZ, provincia Buenos Aires, Argentina, código postal 1636Obispo San Alberto 2975, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419info@companiadehigiene.com.ar81961846001
530583184305MATAFUEGOS ORLANDO S.R.L.02/11/2016Desactualizado Por Documentos Vencidos541146726278En BUENOS AIRES con fecha 25/08/1980Sociedad Responsabilidad Limitada1809.0JUAN F. ARANGUREN 4289, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1407ELSJUAN F ARANGUREN 4289, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1407ELSinfo@orlando-srl.com.ar2509 LIBRO 77 de SRLsin datos
630708538317CAROLS SA09/09/2016Inscripto43019105En Ciudad Autonoma de Buenos Aires * Cap fed con fecha 03/11/2003Sociedad Anónima197137.0Bto.Quinquela Martin 2150, piso N° **, depto N° **, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1296Bto Quinquela Martin 2150, piso N° **, depto N° **, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1296INFO@CAROLS.COM.AR152081729875
730521417311CONFECCIONES JOSE CONTARTESE Y CIA S.R.L.12/09/2016Inscripto0541146534296En CIUDAD AUTONOMA DE BUENOS AIRES con fecha 31/10/1980Sociedad Responsabilidad Limitada1671.0COSTA RICA 5978, piso N° P. BAJA, depto N° 2, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1414BTLCOSTA RICA 5978, piso N° P. BAJA, depto N° 2, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal C1414BTLinfo@contartese.com.arsin datos4336 LIBRO 79 SRL
820305924076Suministros EDA13/10/2016Inscripto45744186sin datosPersona Física483729.0HORTIGUERA 114, piso N° 1º, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1406HORTIGUERA 114, piso N° 1º, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1406sin datossin datossin datos
920082883240SEGUMAX de HORACIO MIGUEL ESPOSITO18/10/2016Desactualizado Por Documentos Vencidos0114831-1535sin datosPersona Física133832.0AYACUCHO 2175, piso N° 3°, depto N° B, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1112ARAOZ 1327, piso N° 2, depto N° A, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1414sin datossin datossin datos
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1006630716032503BIOPAZ S.A.11/06/2021Inscripto3794267234En Corrientes, Capital con fecha 08/05/2018Sociedad Anónima1110000.0RUTA 12 1049, localidad SANTA ANA, departamento SAN COSME, provincia Corrientes, Argentina, código postal 3400Pellegrini 1029, piso N° PA, localidad CORRIENTES, departamento CAPITAL, provincia Corrientes, Argentina, código postal 3400biopazsrl@gmail.com01171, Libro VII, Tomo Isin datos
1006720171591563BIOTECNIKA16/06/2022Inscripto3515553228sin datosPersona Física108334.0Avellaneda 140, piso N° 1, depto N° 9, localidad TEMPERLEY, departamento LOMAS DE ZAMORA, provincia Buenos Aires, Argentina, código postal 1834pasaje Uehara 100, localidad UNQUILLO, departamento COLON, provincia Córdoba, Argentina, código postal 5109sin datossin datossin datos
1006820293290416ELIAS MARTIN SEGURA26/08/2022Inscripto1166048064sin datosPersona Física1327602.0ZAMUDIO 5027, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419ZAMUDIO 5027, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1419sin datossin datossin datos
1006920240423759FEDERICO MARTIN NUÑEZ26/08/2022Inscripto1162520348sin datosPersona Física1321848.0GODOY CRUZ 2449, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1425GODOY CRUZ 2449, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1425sin datossin datossin datos
1007030710308051ZENSEI SRL30/05/2017Inscripto1152914747En Ciudad Autonoma de Buenos Aires con fecha 27/08/2007Sociedad Anónima1884.0concepcion arenal 2978, piso N° pb , depto N° h, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426concepcion arenal 2978, piso N° pb, depto N° h, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426administracion@zensei.com.arsin datos8175 del libro 127 tomo SRL
1007120287286687LAZARTE MARIO03/08/2022Inscripto2235465989sin datosPersona Física3333.0CARAZA 3530, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600CARAZA 3530, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600sin datossin datossin datos
1007230518773743Hotel Astor Sociedad Anonima Comercial25/07/2022Inscripto541143222400En Tigre con fecha 09/12/1968Sociedad Anónima1622.0Azcuenaga 1721, piso N° 6, depto N° G, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1128Entre Rios 1649, localidad MAR DEL PLATA, departamento GENERAL PUEYRREDON, provincia Buenos Aires, Argentina, código postal 7600administracion@comercialdeturismo.comsin datos478847
1007330700503891GIJON SA07/09/2022Inscripto1160190901En CABA con fecha 23/07/1998Sociedad Anónima1850.0VERA PEÑALOZA,ROSARIO BOULEVARD 360, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1107VERA PEÑALOZA,ROSARIO BOULEVARD 360, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1107jdiaz@hotelmadero.comsin datos20959
1007430716441098LISTOS PARA RODAR SAS16/08/2019Inscripto1531111101En BUENOS AIRES con fecha 01/04/2019Otras Formas Societarias6782.0CRAMER 40, piso N° 1, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426CRAMER 40, piso N° 1, localidad Ciudad Autónoma de Buenos Aires, departamento Ciudad Autónoma de Buenos Aires, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1426info@listospararodar.com.ar3071644109830716441098
1007527331126530sin datos18/08/2022Inscripto1167418098sin datosPersona Física8055.0Salcedo 3588, piso N° 1, depto N° C, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1259Salcedo 3588, piso N° 1, depto N° C, localidad CABA, departamento CABA, provincia Ciudad Autónoma de Buenos Aires, Argentina, código postal 1259sin datossin datossin datos